What are the challenges in AI fairness?

What are the main challenges in achieving fairness in AI?

Some main challenges include bias in training data, lack of diverse representation, and the complexity of defining fairness.

How does bias in training data affect AI outcomes?

Bias in training data can lead to skewed results, where the AI may favor certain groups over others, perpetuating inequality.

What steps can be taken to mitigate these biases?

Steps include using diverse datasets, implementing fairness-aware algorithms, and regularly auditing AI systems for bias.

Are there any frameworks for assessing AI fairness?

Yes, there are frameworks like Fairness Indicators and AI Fairness 360 that help assess and improve fairness in AI models.